1. Field of the Invention
This invention relates generally to the field of information storage and retrieval. More particularly it relates to the field of rapid random access storage and retrieval of information, at least one element of which is not easily susceptible to quantitative description. Still more particularly it relates to a method and apparatus for storing and accessing difficult to quantify information and corresponding unique identifier elements in a neural network or simulated neural network computer processing environment. Still more particularly it relates to a method and apparatus for providing automated pattern recognition of certain types of image and analog data. Still more particularly it provides a method and apparatus for storing and retrieving information based upon variable and incomplete human facial image derived information.
2. Description of Related Art
As used herein, collections of information elements form information sets. Distinguishable or distinct information elements are different to the extent that they can be recognized as different from the other information elements in an information set. In order to apply automatic data processing techniques to determine whether an element of an information set is similar or identical to another element of such a set, it is necessary that the characteristics that differentiate the elements of such a set be invariant for each distinct element and communicable to the automatic data processing equipment. Generally, this problem is solved by quantifying the distinguishing characteristics of the elements of the set and sorting or comparing based upon such quantified characteristics.
A. Description of the Problem
Certain types of information are not easily quantifiable. For example, human faces, many types of image data, maps of areas of the globe, seismic traces, voice prints, fingerprints and the like comprise part of a set of visually perceivable data, generally recognizable and distinguishable by skilled humans, but not hitherto subject to effective quantitative identification or automated recognition. Virtually every discipline known to man today acquires vast quantities of image and analog data such as those described above, yet no effective means has existed for automatically recognizing patterns, similarities and identities from large quantities of that data. Modern computers are particularly well suited to the task of storing and retrieving digital data, such as inventory data based upon part numbers. And those same computers are well suited to identifying particular humans based upon unique identifying information such as social security numbers, drivers license numbers and the like. It is desirable, however, to provide a method and apparatus for storing, retrieving and recognizing the inherent unique patterns contained in the aforementioned types of information so that modern automated data processing techniques may be applied to the problems of identifying patterns, identities and similarities in such data.
Particular problems which need solving are: (1) The identification of human beings based upon photographic images of their faces. This problem arises in a number of contexts such as: (a) the identification of missing children, (b) the identification of known criminals from their mug shots, (c) the identification of persons posing a known threat to security (such as known terrorists) based upon prior photographs and captured video frames taken at airports and other areas of surveillance, (d) the verification of the identity of captured criminals based upon prior mug shots, (e) the identification of known criminals, and other individuals based upon previously acquired photographic and/or other image based information, (f) access control to secured areas based upon photographic identity and/or other image based information, (g) authorization to access automated tellers and use credit cards based upon photographic and/or other image based information, and (h) tracking the habits and behavior of key customers based upon photographic and/or other image based information; (2) The identification of photographs and prints of physical evidence such as foot prints, finger prints, tire prints, voice prints, and the like; (3) The identification of analog data such as signatures, seismic traces, and other analog data; (4) The identification of certain known objects within fields of photographic and/or other image based data; and (5) The identification and tracking of photographically perceivable objects.
B. Description of the Prior Art
Prior researchers have attempted to create systems for quantifiably categorizing facial image and analog data but not automated methods for retrieving it from large databases. For example, the "Identikit" is used in criminology to classify human facial images by breaking certain facial features down into a finite number of choices. The choices are then superimposed upon one another to "reconstruct" the human facial image. Other systems have attempted to define the contour lines of a human facial image and identify it based upon that information. Others have taken average gray levels taken from large area of human facial image and attempted to classify and identify human faces based upon such information. Such systems have not proven robust against rotation and translation of the face, expression and perspective changes, the introduction of artifacts such as beards, glasses, etc., nor have they proved practical for use in large databases of human facial images. An example of a very large human facial image database is the F.B.I. mug shot collection which has been estimated to contain over 20 million images. A typical access control system may need to recognize 200 to 1000 persons who have been previously identified as having authorized access to a controlled facility.